AINeutralarXiv – CS AI · Apr 147/10
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Thought Branches: Interpreting LLM Reasoning Requires Resampling
Researchers demonstrate that interpreting large language model reasoning requires analyzing distributions of possible reasoning chains rather than single examples. By resampling text after specific points, they show that stated reasons often don't causally drive model decisions, off-policy interventions are unstable, and hidden contextual hints exert cumulative influence even when explicitly removed.